Glu_FC_Covariation
2024-06-03
Load packages
Set up Environment & Import Data
Note - either import pre-made cestfcmat df or make here.
Remove Values with poor CEST coverage
CEST Density Plots
CEST-FC Scatterplot
##
## Call:
## lm(formula = formula_str, data = graph_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2588 -2.0385 -0.6774 1.7723 8.6459
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.6722 3.8178 3.319 0.00193 **
## age -0.1067 0.1637 -0.652 0.51809
## X52 1.1863 6.8918 0.172 0.86420
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.961 on 40 degrees of freedom
## Multiple R-squared: 0.01052, Adjusted R-squared: -0.03895
## F-statistic: 0.2127 on 2 and 40 DF, p-value: 0.8093
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "PSY", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0132 -2.5455 -0.6675 1.9156 7.9804
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14.3819 5.8545 2.457 0.0224 *
## age -0.1807 0.2511 -0.720 0.4793
## X52 -5.0122 12.2635 -0.409 0.6867
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.554 on 22 degrees of freedom
## Multiple R-squared: 0.03158, Adjusted R-squared: -0.05646
## F-statistic: 0.3587 on 2 and 22 DF, p-value: 0.7026
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "NC", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1957 -1.2998 -0.6481 0.7029 3.8574
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.21480 4.31789 2.829 0.0127 *
## age -0.09954 0.18761 -0.531 0.6035
## X52 8.26362 7.09369 1.165 0.2622
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.98 on 15 degrees of freedom
## Multiple R-squared: 0.08308, Adjusted R-squared: -0.03918
## F-statistic: 0.6795 on 2 and 15 DF, p-value: 0.5218##
## Call:
## lm(formula = formula_str, data = graph_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4955 -0.9337 -0.2379 0.6190 4.3369
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.25347 1.47231 8.323 5.29e-11 ***
## age -0.12852 0.06386 -2.013 0.0496 *
## X54 -0.18686 3.25395 -0.057 0.9544
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.33 on 50 degrees of freedom
## Multiple R-squared: 0.07741, Adjusted R-squared: 0.04051
## F-statistic: 2.098 on 2 and 50 DF, p-value: 0.1334
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "PSY", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1648 -1.2398 -0.2511 0.7529 3.9739
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.68314 2.18742 5.798 3.15e-06 ***
## age -0.15623 0.09443 -1.655 0.109
## X54 -7.72392 5.29825 -1.458 0.156
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.489 on 28 degrees of freedom
## Multiple R-squared: 0.1561, Adjusted R-squared: 0.09579
## F-statistic: 2.589 on 2 and 28 DF, p-value: 0.09296
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "NC", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.0248 -0.6966 -0.2088 0.5622 2.5828
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.12341 1.63814 7.401 5.22e-07 ***
## age -0.12331 0.07211 -1.710 0.1035
## X54 6.77304 3.59133 1.886 0.0747 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9602 on 19 degrees of freedom
## Multiple R-squared: 0.2079, Adjusted R-squared: 0.1246
## F-statistic: 2.494 on 2 and 19 DF, p-value: 0.1092##
## Call:
## lm(formula = formula_str, data = graph_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5601 -1.0399 -0.2810 0.8848 2.3923
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.18507 1.67247 5.492 3.32e-06 ***
## age -0.04503 0.07213 -0.624 0.536
## X55 1.38931 4.47119 0.311 0.758
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.295 on 36 degrees of freedom
## Multiple R-squared: 0.01219, Adjusted R-squared: -0.04268
## F-statistic: 0.2222 on 2 and 36 DF, p-value: 0.8019
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "PSY", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3427 -1.0574 -0.3604 1.1319 2.2185
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.25943 2.44674 3.784 0.00109 **
## age -0.05527 0.10234 -0.540 0.59487
## X55 0.51147 6.48029 0.079 0.93784
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.403 on 21 degrees of freedom
## Multiple R-squared: 0.01438, Adjusted R-squared: -0.07948
## F-statistic: 0.1532 on 2 and 21 DF, p-value: 0.8589
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "NC", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3412 -0.7946 -0.0431 0.4583 2.0823
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.06856 2.39852 3.781 0.00262 **
## age -0.02758 0.10773 -0.256 0.80231
## X55 2.39046 6.48127 0.369 0.71868
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.162 on 12 degrees of freedom
## Multiple R-squared: 0.0124, Adjusted R-squared: -0.1522
## F-statistic: 0.07535 on 2 and 12 DF, p-value: 0.9279##
## Call:
## lm(formula = formula_str, data = graph_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4606 -1.3294 -0.3652 0.9298 3.8331
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.04873 1.85318 6.502 3.61e-08 ***
## age -0.12654 0.08091 -1.564 0.124
## X56 4.41771 4.91941 0.898 0.373
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.674 on 50 degrees of freedom
## Multiple R-squared: 0.05323, Adjusted R-squared: 0.01536
## F-statistic: 1.406 on 2 and 50 DF, p-value: 0.2547
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "PSY", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5840 -1.7164 -0.6351 1.7558 3.6972
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.1863 2.9107 4.187 0.000254 ***
## age -0.1264 0.1262 -1.002 0.324972
## X56 3.9406 8.3633 0.471 0.641168
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.992 on 28 degrees of freedom
## Multiple R-squared: 0.04086, Adjusted R-squared: -0.02765
## F-statistic: 0.5965 on 2 and 28 DF, p-value: 0.5576
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "NC", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.7366 -0.9180 -0.0678 0.6953 2.3104
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.27202 2.09702 5.852 1.23e-05 ***
## age -0.14727 0.09342 -1.576 0.131
## X56 6.87563 5.40434 1.272 0.219
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.198 on 19 degrees of freedom
## Multiple R-squared: 0.1356, Adjusted R-squared: 0.04463
## F-statistic: 1.491 on 2 and 19 DF, p-value: 0.2504##
## Call:
## lm(formula = formula_str, data = graph_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.02025 -0.48500 0.01973 0.54665 1.80700
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.72984 0.98977 9.830 3.54e-13 ***
## age -0.09960 0.04331 -2.300 0.0258 *
## X60 1.69106 2.32599 0.727 0.4707
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8959 on 49 degrees of freedom
## Multiple R-squared: 0.1004, Adjusted R-squared: 0.06365
## F-statistic: 2.733 on 2 and 49 DF, p-value: 0.07492
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "PSY", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0130 -0.7482 0.1332 0.6207 1.8194
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.77508 1.43762 6.104 1.38e-06 ***
## age -0.05945 0.06135 -0.969 0.341
## X60 0.61473 3.23851 0.190 0.851
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9669 on 28 degrees of freedom
## Multiple R-squared: 0.03461, Adjusted R-squared: -0.03435
## F-statistic: 0.5018 on 2 and 28 DF, p-value: 0.6108
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "NC", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.34686 -0.50415 0.07637 0.54653 1.45607
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.57785 1.36154 8.504 1.02e-07 ***
## age -0.18300 0.06191 -2.956 0.00846 **
## X60 5.47620 3.40743 1.607 0.12542
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7692 on 18 degrees of freedom
## Multiple R-squared: 0.3313, Adjusted R-squared: 0.257
## F-statistic: 4.458 on 2 and 18 DF, p-value: 0.02675##
## Call:
## lm(formula = formula_str, data = graph_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6007 -0.6908 -0.2193 0.4912 3.0850
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.47980 1.27742 8.204 1.09e-10 ***
## age -0.09559 0.05656 -1.690 0.0975 .
## X69 -4.18832 3.17211 -1.320 0.1930
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.098 on 48 degrees of freedom
## Multiple R-squared: 0.1187, Adjusted R-squared: 0.08199
## F-statistic: 3.233 on 2 and 48 DF, p-value: 0.04818
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "PSY", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5209 -0.5371 -0.1061 0.3650 2.8851
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.9558 1.5936 6.875 1.8e-07 ***
## age -0.1101 0.0705 -1.561 0.1297
## X69 -7.0200 4.0909 -1.716 0.0972 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.075 on 28 degrees of freedom
## Multiple R-squared: 0.2059, Adjusted R-squared: 0.1492
## F-statistic: 3.629 on 2 and 28 DF, p-value: 0.03967
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "NC", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6323 -0.8472 -0.2998 0.5146 2.2342
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.80830 2.21559 4.427 0.000369 ***
## age -0.07663 0.09823 -0.780 0.446051
## X69 -0.35016 5.27525 -0.066 0.947851
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.178 on 17 degrees of freedom
## Multiple R-squared: 0.04129, Adjusted R-squared: -0.0715
## F-statistic: 0.3661 on 2 and 17 DF, p-value: 0.6988##
## Call:
## lm(formula = formula_str, data = graph_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.58080 -0.43055 -0.05936 0.47751 1.93996
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.21523 0.70318 14.527 <2e-16 ***
## age -0.07630 0.03153 -2.420 0.0193 *
## X73 -2.33010 2.13479 -1.091 0.2804
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6344 on 49 degrees of freedom
## Multiple R-squared: 0.1566, Adjusted R-squared: 0.1222
## F-statistic: 4.55 on 2 and 49 DF, p-value: 0.01539
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "PSY", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5927 -0.3389 -0.1474 0.4044 1.7995
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.86969 1.06284 10.227 5.85e-11 ***
## age -0.10336 0.04652 -2.222 0.0345 *
## X73 -4.03388 4.02491 -1.002 0.3248
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7256 on 28 degrees of freedom
## Multiple R-squared: 0.1956, Adjusted R-squared: 0.1381
## F-statistic: 3.404 on 2 and 28 DF, p-value: 0.04749
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "NC", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8878 -0.2144 -0.0964 0.3731 0.8406
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.39995 0.81386 11.550 9.32e-10 ***
## age -0.03662 0.03709 -0.988 0.336
## X73 -2.15335 2.03985 -1.056 0.305
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4695 on 18 degrees of freedom
## Multiple R-squared: 0.1562, Adjusted R-squared: 0.06245
## F-statistic: 1.666 on 2 and 18 DF, p-value: 0.2168##
## Call:
## lm(formula = formula_str, data = graph_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.29616 -0.47340 -0.03904 0.35317 1.60225
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.93245 0.83449 10.704 2.61e-14 ***
## age -0.05932 0.03685 -1.610 0.114
## X74 3.48389 2.07714 1.677 0.100 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7546 on 48 degrees of freedom
## Multiple R-squared: 0.08639, Adjusted R-squared: 0.04832
## F-statistic: 2.269 on 2 and 48 DF, p-value: 0.1144
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "PSY", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.13657 -0.54719 0.09621 0.36211 1.58776
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.558683 1.115445 6.776 2.81e-07 ***
## age -0.001612 0.048791 -0.033 0.974
## X74 2.711060 2.887418 0.939 0.356
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7635 on 27 degrees of freedom
## Multiple R-squared: 0.0319, Adjusted R-squared: -0.03981
## F-statistic: 0.4449 on 2 and 27 DF, p-value: 0.6455
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "NC", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.44979 -0.36496 0.06795 0.36518 1.20414
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.84892 1.19729 9.061 3.98e-08 ***
## age -0.14067 0.05347 -2.631 0.017 *
## X74 4.84150 2.84562 1.701 0.106
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7035 on 18 degrees of freedom
## Multiple R-squared: 0.3079, Adjusted R-squared: 0.231
## F-statistic: 4.003 on 2 and 18 DF, p-value: 0.03646##
## Call:
## lm(formula = formula_str, data = graph_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9961 -0.4039 0.0059 0.4824 1.3567
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.66020 0.75368 11.491 1.22e-15 ***
## age -0.05073 0.03287 -1.543 0.129
## X77 1.76387 1.88187 0.937 0.353
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6899 on 50 degrees of freedom
## Multiple R-squared: 0.05662, Adjusted R-squared: 0.01888
## F-statistic: 1.5 on 2 and 50 DF, p-value: 0.2329
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "PSY", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.00102 -0.26108 0.04178 0.45037 1.13691
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.508874 1.079211 6.958 1.45e-07 ***
## age -0.002108 0.046734 -0.045 0.964
## X77 4.262515 3.223054 1.323 0.197
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7381 on 28 degrees of freedom
## Multiple R-squared: 0.05889, Adjusted R-squared: -0.008329
## F-statistic: 0.8761 on 2 and 28 DF, p-value: 0.4275
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "NC", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.18304 -0.22776 0.07674 0.27727 1.18595
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99072 0.99518 10.039 4.94e-09 ***
## age -0.10807 0.04378 -2.468 0.0232 *
## X77 0.75436 2.12503 0.355 0.7265
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5986 on 19 degrees of freedom
## Multiple R-squared: 0.2435, Adjusted R-squared: 0.1639
## F-statistic: 3.059 on 2 and 19 DF, p-value: 0.07054##
## Call:
## lm(formula = formula_str, data = graph_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5643 -0.8766 0.1701 0.9539 2.4067
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.2779 2.3648 1.809 0.0825 .
## age 0.1233 0.1050 1.174 0.2513
## X78 9.5729 6.1412 1.559 0.1316
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.343 on 25 degrees of freedom
## Multiple R-squared: 0.1382, Adjusted R-squared: 0.06922
## F-statistic: 2.004 on 2 and 25 DF, p-value: 0.1559
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "PSY", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4806 -0.7151 0.1197 0.7220 1.1511
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.35563 2.86179 1.871 0.0859 .
## age 0.08657 0.13001 0.666 0.5181
## X78 3.09607 5.01629 0.617 0.5486
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8821 on 12 degrees of freedom
## Multiple R-squared: 0.04871, Adjusted R-squared: -0.1098
## F-statistic: 0.3073 on 2 and 12 DF, p-value: 0.7411
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "NC", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7119 -1.0611 0.3185 0.9461 2.5276
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.47228 5.08018 1.274 0.231
## age 0.02996 0.20795 0.144 0.888
## X78 27.08856 20.94504 1.293 0.225
##
## Residual standard error: 1.705 on 10 degrees of freedom
## Multiple R-squared: 0.239, Adjusted R-squared: 0.08682
## F-statistic: 1.57 on 2 and 10 DF, p-value: 0.2552##
## Call:
## lm(formula = formula_str, data = graph_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8249 -0.5299 -0.0542 0.4267 1.7789
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.82728 0.90798 9.722 6.32e-13 ***
## age -0.03759 0.03964 -0.948 0.348
## X87 0.89204 2.31847 0.385 0.702
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8216 on 48 degrees of freedom
## Multiple R-squared: 0.02026, Adjusted R-squared: -0.02056
## F-statistic: 0.4963 on 2 and 48 DF, p-value: 0.6119
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "PSY", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6482 -0.4748 -0.1116 0.4754 1.9641
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.17641 1.26281 6.475 7.32e-07 ***
## age -0.01016 0.05504 -0.185 0.855
## X87 2.89021 3.97285 0.727 0.473
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8485 on 26 degrees of freedom
## Multiple R-squared: 0.02128, Adjusted R-squared: -0.05401
## F-statistic: 0.2827 on 2 and 26 DF, p-value: 0.7561
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "NC", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.31086 -0.56337 0.07528 0.38087 1.58975
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.53883 1.37500 6.937 1.3e-06 ***
## age -0.06772 0.06011 -1.127 0.274
## X87 0.02106 2.95209 0.007 0.994
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8281 on 19 degrees of freedom
## Multiple R-squared: 0.06413, Adjusted R-squared: -0.03438
## F-statistic: 0.651 on 2 and 19 DF, p-value: 0.5328##
## Call:
## lm(formula = formula_str, data = graph_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3157 -0.6779 -0.1836 0.3957 2.1509
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.30612 0.95656 10.774 1.6e-14 ***
## age -0.07991 0.04177 -1.913 0.0616 .
## X88 1.48745 2.57400 0.578 0.5660
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8591 on 49 degrees of freedom
## Multiple R-squared: 0.07062, Adjusted R-squared: 0.03269
## F-statistic: 1.862 on 2 and 49 DF, p-value: 0.1662
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "PSY", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.1253 -0.5351 -0.1004 0.3838 2.0579
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.57550 1.26056 8.389 3.99e-09 ***
## age -0.08883 0.05449 -1.630 0.114
## X88 -2.28791 4.74645 -0.482 0.634
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8603 on 28 degrees of freedom
## Multiple R-squared: 0.09213, Adjusted R-squared: 0.02728
## F-statistic: 1.421 on 2 and 28 DF, p-value: 0.2584
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "NC", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3441 -0.6390 0.1206 0.3982 1.8679
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.27690 1.59730 6.434 4.69e-06 ***
## age -0.08133 0.07045 -1.154 0.263
## X88 3.10607 3.38767 0.917 0.371
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9049 on 18 degrees of freedom
## Multiple R-squared: 0.08377, Adjusted R-squared: -0.01803
## F-statistic: 0.8229 on 2 and 18 DF, p-value: 0.455##
## Call:
## lm(formula = formula_str, data = graph_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.2622 -0.4497 -0.0623 0.4409 1.5304
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.64061 0.71943 13.400 <2e-16 ***
## age -0.04911 0.03126 -1.571 0.1224
## X91 3.30331 1.77885 1.857 0.0692 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6582 on 50 degrees of freedom
## Multiple R-squared: 0.1007, Adjusted R-squared: 0.06473
## F-statistic: 2.799 on 2 and 50 DF, p-value: 0.07041
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "PSY", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.31263 -0.43830 -0.00099 0.34518 1.48773
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.51150 1.04885 9.069 7.96e-10 ***
## age -0.04085 0.04562 -0.895 0.378
## X91 2.73020 2.65505 1.028 0.313
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7089 on 28 degrees of freedom
## Multiple R-squared: 0.0747, Adjusted R-squared: 0.00861
## F-statistic: 1.13 on 2 and 28 DF, p-value: 0.3372
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "NC", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.16974 -0.42055 -0.08608 0.40011 1.09914
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.08941 1.06879 9.440 1.32e-08 ***
## age -0.07280 0.04668 -1.560 0.1353
## X91 4.73494 2.59310 1.826 0.0836 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6105 on 19 degrees of freedom
## Multiple R-squared: 0.1845, Adjusted R-squared: 0.09861
## F-statistic: 2.149 on 2 and 19 DF, p-value: 0.1441##
## Call:
## lm(formula = formula_str, data = graph_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.82522 -0.55652 -0.03506 0.65108 2.14132
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.36764 1.04521 9.919 3.3e-13 ***
## age -0.14585 0.04514 -3.231 0.00223 **
## X92 5.73249 2.41862 2.370 0.02185 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9197 on 48 degrees of freedom
## Multiple R-squared: 0.2373, Adjusted R-squared: 0.2055
## F-statistic: 7.465 on 2 and 48 DF, p-value: 0.001503
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "PSY", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8402 -0.5355 0.1245 0.4571 2.2789
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.82234 1.33699 7.347 6.67e-08 ***
## age -0.12331 0.05774 -2.135 0.0419 *
## X92 4.87911 3.05431 1.597 0.1218
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8824 on 27 degrees of freedom
## Multiple R-squared: 0.2294, Adjusted R-squared: 0.1723
## F-statistic: 4.019 on 2 and 27 DF, p-value: 0.02967
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "NC", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9210 -0.7479 0.2640 0.8638 1.1583
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.45835 1.87874 6.099 9.21e-06 ***
## age -0.19178 0.08131 -2.359 0.0298 *
## X92 7.97377 4.43078 1.800 0.0887 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.024 on 18 degrees of freedom
## Multiple R-squared: 0.2698, Adjusted R-squared: 0.1887
## F-statistic: 3.326 on 2 and 18 DF, p-value: 0.059##
## Call:
## lm(formula = formula_str, data = graph_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4114 -0.2749 0.1166 0.4879 1.1598
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.39419 0.81834 10.258 1.4e-13 ***
## age -0.05515 0.03544 -1.556 0.126
## X93 -1.66943 1.68336 -0.992 0.326
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7228 on 47 degrees of freedom
## Multiple R-squared: 0.06429, Adjusted R-squared: 0.02447
## F-statistic: 1.615 on 2 and 47 DF, p-value: 0.2098
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "PSY", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2404 -0.2390 0.1305 0.4251 1.4323
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.86776 1.12461 7.885 2.32e-08 ***
## age -0.07938 0.04906 -1.618 0.1177
## X93 -4.27348 2.19201 -1.950 0.0621 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7383 on 26 degrees of freedom
## Multiple R-squared: 0.1705, Adjusted R-squared: 0.1066
## F-statistic: 2.671 on 2 and 26 DF, p-value: 0.08809
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "NC", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.43050 -0.32536 -0.07651 0.35767 1.12994
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.49408 1.20488 7.050 1.41e-06 ***
## age -0.05219 0.05135 -1.016 0.323
## X93 2.60783 2.68974 0.970 0.345
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6813 on 18 degrees of freedom
## Multiple R-squared: 0.08634, Adjusted R-squared: -0.01518
## F-statistic: 0.8505 on 2 and 18 DF, p-value: 0.4437##
## Call:
## lm(formula = formula_str, data = graph_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3536 -0.5691 -0.0912 0.6574 3.6698
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.60236 1.33164 6.460 7.83e-08 ***
## age -0.01517 0.05729 -0.265 0.792
## X96 0.37896 3.44385 0.110 0.913
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.125 on 43 degrees of freedom
## Multiple R-squared: 0.001927, Adjusted R-squared: -0.0445
## F-statistic: 0.0415 on 2 and 43 DF, p-value: 0.9594
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "PSY", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.7823 -0.6640 0.2707 0.6610 1.8226
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.79091 1.54339 5.696 6.23e-06 ***
## age -0.02522 0.06568 -0.384 0.704
## X96 -3.42950 3.79385 -0.904 0.375
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9607 on 25 degrees of freedom
## Multiple R-squared: 0.03216, Adjusted R-squared: -0.04527
## F-statistic: 0.4153 on 2 and 25 DF, p-value: 0.6646
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "NC", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2184 -0.7637 -0.2319 0.8321 2.8872
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.5346 2.8169 3.740 0.00197 **
## age -0.1031 0.1264 -0.816 0.42748
## X96 9.5932 8.2828 1.158 0.26489
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.343 on 15 degrees of freedom
## Multiple R-squared: 0.08637, Adjusted R-squared: -0.03545
## F-statistic: 0.709 on 2 and 15 DF, p-value: 0.5079
CEST-Age Scatterplot
## [1] "NZMean_52"
## [1] 1
##
## Call:
## lm(formula = formula_str, data = graph_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1952 -2.0617 -0.5824 1.8089 8.5760
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.48210 3.61103 3.457 0.00129 **
## age -0.09985 0.15683 -0.637 0.52789
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.925 on 41 degrees of freedom
## Multiple R-squared: 0.009789, Adjusted R-squared: -0.01436
## F-statistic: 0.4053 on 1 and 41 DF, p-value: 0.5279
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "PSY", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.3692 -2.8006 -0.7191 1.7114 8.0832
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14.7066 5.6943 2.583 0.0166 *
## age -0.1860 0.2461 -0.756 0.4575
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.489 on 23 degrees of freedom
## Multiple R-squared: 0.02423, Adjusted R-squared: -0.0182
## F-statistic: 0.571 on 1 and 23 DF, p-value: 0.4575
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "NC", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2250 -1.4023 -0.4332 0.7432 3.3737
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.707972 3.784990 2.565 0.0208 *
## age 0.007312 0.165480 0.044 0.9653
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.001 on 16 degrees of freedom
## Multiple R-squared: 0.000122, Adjusted R-squared: -0.06237
## F-statistic: 0.001952 on 1 and 16 DF, p-value: 0.9653## [1] "NZMean_54"
## [1] 1
##
## Call:
## lm(formula = formula_str, data = graph_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4988 -0.9220 -0.2368 0.5849 4.3453
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.26799 1.43617 8.542 2.09e-11 ***
## age -0.12910 0.06244 -2.068 0.0438 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.317 on 51 degrees of freedom
## Multiple R-squared: 0.07735, Adjusted R-squared: 0.05926
## F-statistic: 4.276 on 1 and 51 DF, p-value: 0.04375
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "PSY", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4217 -1.1132 -0.2611 0.6510 4.2695
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13.03377 2.21594 5.882 2.2e-06 ***
## age -0.16467 0.09606 -1.714 0.0972 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.517 on 29 degrees of freedom
## Multiple R-squared: 0.09201, Adjusted R-squared: 0.0607
## F-statistic: 2.939 on 1 and 29 DF, p-value: 0.09715
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "NC", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.1788 -0.8474 -0.2117 0.4959 2.3121
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.27185 1.67231 6.740 1.48e-06 ***
## age -0.08224 0.07300 -1.127 0.273
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.02 on 20 degrees of freedom
## Multiple R-squared: 0.05967, Adjusted R-squared: 0.01265
## F-statistic: 1.269 on 1 and 20 DF, p-value: 0.2733## [1] "NZMean_55"
## [1] 1
##
## Call:
## lm(formula = formula_str, data = graph_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5783 -1.0653 -0.2623 0.8339 2.4316
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.18365 1.65192 5.559 2.48e-06 ***
## age -0.04220 0.07067 -0.597 0.554
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.279 on 37 degrees of freedom
## Multiple R-squared: 0.009544, Adjusted R-squared: -0.01722
## F-statistic: 0.3565 on 1 and 37 DF, p-value: 0.5541
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "PSY", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3465 -1.0449 -0.3846 1.1073 2.2402
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.29855 2.34127 3.972 0.000646 ***
## age -0.05590 0.09969 -0.561 0.580627
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.371 on 22 degrees of freedom
## Multiple R-squared: 0.01409, Adjusted R-squared: -0.03072
## F-statistic: 0.3145 on 1 and 22 DF, p-value: 0.5806
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "NC", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.42415 -0.71506 0.01333 0.41140 2.10109
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.81670 2.22155 3.969 0.0016 **
## age -0.01200 0.09576 -0.125 0.9022
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.122 on 13 degrees of freedom
## Multiple R-squared: 0.001207, Adjusted R-squared: -0.07562
## F-statistic: 0.01572 on 1 and 13 DF, p-value: 0.9022## [1] "NZMean_56"
## [1] 1
##
## Call:
## lm(formula = formula_str, data = graph_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.350 -1.257 -0.245 1.098 3.887
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.7609 1.8218 6.456 3.94e-08 ***
## age -0.1124 0.0792 -1.419 0.162
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.671 on 51 degrees of freedom
## Multiple R-squared: 0.03796, Adjusted R-squared: 0.0191
## F-statistic: 2.012 on 1 and 51 DF, p-value: 0.1621
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "PSY", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4590 -1.6998 -0.4877 1.8290 3.7410
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.1355 2.8694 4.229 0.000214 ***
## age -0.1242 0.1244 -0.999 0.326125
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.965 on 29 degrees of freedom
## Multiple R-squared: 0.03326, Adjusted R-squared: -7.603e-05
## F-statistic: 0.9977 on 1 and 29 DF, p-value: 0.3261
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "NC", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.73509 -0.89526 -0.04446 0.56768 2.16920
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.33770 1.99437 5.685 1.46e-05 ***
## age -0.10008 0.08706 -1.150 0.264
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.216 on 20 degrees of freedom
## Multiple R-squared: 0.06198, Adjusted R-squared: 0.01508
## F-statistic: 1.322 on 1 and 20 DF, p-value: 0.2639## [1] "NZMean_60"
## [1] 1
##
## Call:
## lm(formula = formula_str, data = graph_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.11944 -0.53949 0.09176 0.57798 1.90759
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.72366 0.98506 9.871 2.48e-13 ***
## age -0.09538 0.04272 -2.233 0.0301 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8917 on 50 degrees of freedom
## Multiple R-squared: 0.09066, Adjusted R-squared: 0.07248
## F-statistic: 4.985 on 1 and 50 DF, p-value: 0.03007
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "PSY", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0483 -0.7545 0.1416 0.6548 1.7698
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.82620 1.38851 6.357 6.02e-07 ***
## age -0.06022 0.06019 -1.000 0.325
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9507 on 29 degrees of freedom
## Multiple R-squared: 0.03336, Adjusted R-squared: 3.117e-05
## F-statistic: 1.001 on 1 and 29 DF, p-value: 0.3254
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "NC", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.51331 -0.60232 -0.09635 0.36032 1.78255
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.92224 1.35202 8.078 1.45e-07 ***
## age -0.14185 0.05867 -2.418 0.0258 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8006 on 19 degrees of freedom
## Multiple R-squared: 0.2353, Adjusted R-squared: 0.1951
## F-statistic: 5.846 on 1 and 19 DF, p-value: 0.02582## [1] "NZMean_69"
## [1] 1
##
## Call:
## lm(formula = formula_str, data = graph_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.7292 -0.7179 -0.0907 0.5521 3.1234
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.81716 1.26106 8.578 2.53e-11 ***
## age -0.11751 0.05448 -2.157 0.036 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.106 on 49 degrees of freedom
## Multiple R-squared: 0.0867, Adjusted R-squared: 0.06806
## F-statistic: 4.652 on 1 and 49 DF, p-value: 0.03596
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "PSY", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.7595 -0.5618 -0.1230 0.4569 2.9568
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.41671 1.62256 7.036 9.72e-08 ***
## age -0.14143 0.07034 -2.011 0.0537 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.111 on 29 degrees of freedom
## Multiple R-squared: 0.1224, Adjusted R-squared: 0.09209
## F-statistic: 4.043 on 1 and 29 DF, p-value: 0.05373
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "NC", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6543 -0.8413 -0.2978 0.5203 2.2322
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.84379 2.08980 4.710 0.000174 ***
## age -0.07884 0.08982 -0.878 0.391689
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.145 on 18 degrees of freedom
## Multiple R-squared: 0.04104, Adjusted R-squared: -0.01224
## F-statistic: 0.7703 on 1 and 18 DF, p-value: 0.3917## [1] "NZMean_73"
## [1] 1
##
## Call:
## lm(formula = formula_str, data = graph_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.55301 -0.31899 -0.03307 0.37924 2.00967
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.27841 0.70213 14.639 < 2e-16 ***
## age -0.08547 0.03045 -2.807 0.00711 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6356 on 50 degrees of freedom
## Multiple R-squared: 0.1361, Adjusted R-squared: 0.1189
## F-statistic: 7.879 on 1 and 50 DF, p-value: 0.00711
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "PSY", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5347 -0.3762 -0.1440 0.4106 1.9549
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.78952 1.05990 10.180 4.4e-11 ***
## age -0.11068 0.04595 -2.409 0.0226 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7257 on 29 degrees of freedom
## Multiple R-squared: 0.1667, Adjusted R-squared: 0.138
## F-statistic: 5.803 on 1 and 29 DF, p-value: 0.02257
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "NC", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.90207 -0.29633 -0.07935 0.38386 0.86686
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.59371 0.79527 12.063 2.37e-10 ***
## age -0.05124 0.03451 -1.485 0.154
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4709 on 19 degrees of freedom
## Multiple R-squared: 0.104, Adjusted R-squared: 0.05681
## F-statistic: 2.205 on 1 and 19 DF, p-value: 0.154## [1] "NZMean_74"
## [1] 1
##
## Call:
## lm(formula = formula_str, data = graph_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.29455 -0.55790 -0.02115 0.39419 1.68832
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.87132 0.84898 10.45 4.58e-14 ***
## age -0.04751 0.03683 -1.29 0.203
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7685 on 49 degrees of freedom
## Multiple R-squared: 0.03284, Adjusted R-squared: 0.0131
## F-statistic: 1.664 on 1 and 49 DF, p-value: 0.2031
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "PSY", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.1352 -0.5027 0.1200 0.3581 1.6661
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.580915 1.112832 6.812 2.12e-07 ***
## age 0.004373 0.048270 0.091 0.928
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7618 on 28 degrees of freedom
## Multiple R-squared: 0.000293, Adjusted R-squared: -0.03541
## F-statistic: 0.008205 on 1 and 28 DF, p-value: 0.9285
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "NC", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.33207 -0.52996 0.02688 0.36698 1.39466
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.59749 1.24597 8.505 6.67e-08 ***
## age -0.11656 0.05407 -2.156 0.0441 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7378 on 19 degrees of freedom
## Multiple R-squared: 0.1965, Adjusted R-squared: 0.1543
## F-statistic: 4.648 on 1 and 19 DF, p-value: 0.04412## [1] "NZMean_77"
## [1] 1
##
## Call:
## lm(formula = formula_str, data = graph_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0155 -0.3010 0.0024 0.4526 1.4179
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.61676 0.75135 11.468 9.85e-16 ***
## age -0.04764 0.03266 -1.459 0.151
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6891 on 51 degrees of freedom
## Multiple R-squared: 0.04004, Adjusted R-squared: 0.02122
## F-statistic: 2.127 on 1 and 51 DF, p-value: 0.1508
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "PSY", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.03167 -0.33497 -0.00438 0.38447 1.31613
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.575025 1.091884 6.938 1.26e-07 ***
## age -0.002624 0.047332 -0.055 0.956
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7476 on 29 degrees of freedom
## Multiple R-squared: 0.000106, Adjusted R-squared: -0.03437
## F-statistic: 0.003073 on 1 and 29 DF, p-value: 0.9562
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "NC", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.18376 -0.22579 0.05526 0.27598 1.20372
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93269 0.95997 10.347 1.78e-09 ***
## age -0.10489 0.04191 -2.503 0.0211 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5853 on 20 degrees of freedom
## Multiple R-squared: 0.2385, Adjusted R-squared: 0.2005
## F-statistic: 6.265 on 1 and 20 DF, p-value: 0.0211## [1] "NZMean_78"
## [1] 1
##
## Call:
## lm(formula = formula_str, data = graph_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4690 -0.5532 -0.0179 0.8838 3.8966
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.47997 1.52980 4.889 1.13e-05 ***
## age -0.02893 0.06612 -0.438 0.664
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.351 on 49 degrees of freedom
## Multiple R-squared: 0.003893, Adjusted R-squared: -0.01644
## F-statistic: 0.1915 on 1 and 49 DF, p-value: 0.6636
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "PSY", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4914 -0.5753 0.1175 0.8742 1.6883
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.81659 1.72334 5.696 4.16e-06 ***
## age -0.12345 0.07434 -1.661 0.108
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.144 on 28 degrees of freedom
## Multiple R-squared: 0.08965, Adjusted R-squared: 0.05714
## F-statistic: 2.757 on 1 and 28 DF, p-value: 0.108
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "NC", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0612 -1.3203 0.0227 1.0785 3.3541
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.48569 2.67607 1.676 0.110
## age 0.09177 0.11596 0.791 0.438
##
## Residual standard error: 1.559 on 19 degrees of freedom
## Multiple R-squared: 0.03192, Adjusted R-squared: -0.01904
## F-statistic: 0.6264 on 1 and 19 DF, p-value: 0.4385## [1] "NZMean_87"
## [1] 1
##
## Call:
## lm(formula = formula_str, data = graph_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.87144 -0.51472 0.00192 0.41888 1.76781
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.80110 0.89752 9.806 3.84e-13 ***
## age -0.03630 0.03915 -0.927 0.358
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8145 on 49 degrees of freedom
## Multiple R-squared: 0.01724, Adjusted R-squared: -0.002819
## F-statistic: 0.8595 on 1 and 49 DF, p-value: 0.3584
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "PSY", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.80776 -0.45411 0.00869 0.37145 1.90643
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.19477 1.25151 6.548 5.06e-07 ***
## age -0.01045 0.05456 -0.192 0.849
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8411 on 27 degrees of freedom
## Multiple R-squared: 0.001358, Adjusted R-squared: -0.03563
## F-statistic: 0.03672 on 1 and 27 DF, p-value: 0.8495
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "NC", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.31106 -0.56358 0.07676 0.38116 1.58995
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.53729 1.32375 7.205 5.65e-07 ***
## age -0.06765 0.05779 -1.171 0.255
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8071 on 20 degrees of freedom
## Multiple R-squared: 0.06413, Adjusted R-squared: 0.01734
## F-statistic: 1.371 on 1 and 20 DF, p-value: 0.2555## [1] "NZMean_88"
## [1] 1
##
## Call:
## lm(formula = formula_str, data = graph_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.2605 -0.6695 -0.1306 0.4076 2.1013
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.23714 0.94274 10.859 9.34e-15 ***
## age -0.07578 0.04088 -1.853 0.0697 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8534 on 50 degrees of freedom
## Multiple R-squared: 0.06429, Adjusted R-squared: 0.04557
## F-statistic: 3.435 on 1 and 50 DF, p-value: 0.06972
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "PSY", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.0677 -0.5290 -0.1665 0.4151 2.1122
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.52628 1.23968 8.491 2.35e-09 ***
## age -0.08797 0.05374 -1.637 0.112
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8488 on 29 degrees of freedom
## Multiple R-squared: 0.0846, Adjusted R-squared: 0.05303
## F-statistic: 2.68 on 1 and 29 DF, p-value: 0.1124
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "NC", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.25811 -0.74182 0.01342 0.37613 1.93043
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.85064 1.52172 6.473 3.33e-06 ***
## age -0.05950 0.06603 -0.901 0.379
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9011 on 19 degrees of freedom
## Multiple R-squared: 0.04098, Adjusted R-squared: -0.009491
## F-statistic: 0.812 on 1 and 19 DF, p-value: 0.3788## [1] "NZMean_91"
## [1] 1
##
## Call:
## lm(formula = formula_str, data = graph_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.27626 -0.45036 -0.04829 0.34549 1.72972
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.54927 0.73477 12.996 <2e-16 ***
## age -0.04576 0.03194 -1.432 0.158
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6739 on 51 degrees of freedom
## Multiple R-squared: 0.03868, Adjusted R-squared: 0.01983
## F-statistic: 2.052 on 1 and 51 DF, p-value: 0.1581
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "PSY", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.33791 -0.46644 0.04102 0.27926 1.68126
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.68385 1.03639 9.344 3.01e-10 ***
## age -0.04923 0.04493 -1.096 0.282
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7096 on 29 degrees of freedom
## Multiple R-squared: 0.03976, Adjusted R-squared: 0.006648
## F-statistic: 1.201 on 1 and 29 DF, p-value: 0.2822
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "NC", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.1162 -0.4773 -0.1217 0.4867 1.0171
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.40656 1.05804 8.891 2.2e-08 ***
## age -0.04289 0.04619 -0.929 0.364
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6451 on 20 degrees of freedom
## Multiple R-squared: 0.04134, Adjusted R-squared: -0.006594
## F-statistic: 0.8624 on 1 and 20 DF, p-value: 0.3641## [1] "NZMean_92"
## [1] 1
##
## Call:
## lm(formula = formula_str, data = graph_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.98701 -0.68943 0.04373 0.57165 2.73288
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.15208 1.08921 9.321 1.97e-12 ***
## age -0.13733 0.04707 -2.917 0.00531 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9621 on 49 degrees of freedom
## Multiple R-squared: 0.148, Adjusted R-squared: 0.1306
## F-statistic: 8.511 on 1 and 49 DF, p-value: 0.005314
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "PSY", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9622 -0.6337 0.0397 0.4808 2.7635
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.0549 1.3654 7.364 5.1e-08 ***
## age -0.1343 0.0589 -2.280 0.0304 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9065 on 28 degrees of freedom
## Multiple R-squared: 0.1566, Adjusted R-squared: 0.1264
## F-statistic: 5.197 on 1 and 28 DF, p-value: 0.03044
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "NC", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.78779 -0.84970 0.04414 1.01407 1.56951
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.26990 1.85958 5.523 2.51e-05 ***
## age -0.14080 0.08058 -1.747 0.0967 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.083 on 19 degrees of freedom
## Multiple R-squared: 0.1385, Adjusted R-squared: 0.09311
## F-statistic: 3.053 on 1 and 19 DF, p-value: 0.09672## [1] "NZMean_93"
## [1] 1
##
## Call:
## lm(formula = formula_str, data = graph_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4985 -0.2243 0.0570 0.5289 1.0813
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.40032 0.81818 10.267 1.06e-13 ***
## age -0.05301 0.03537 -1.499 0.14
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7227 on 48 degrees of freedom
## Multiple R-squared: 0.04471, Adjusted R-squared: 0.0248
## F-statistic: 2.246 on 1 and 48 DF, p-value: 0.1405
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "PSY", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4756 -0.2110 0.2318 0.5151 0.9402
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.54054 1.16827 7.310 7.3e-08 ***
## age -0.05959 0.05042 -1.182 0.248
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7756 on 27 degrees of freedom
## Multiple R-squared: 0.04919, Adjusted R-squared: 0.01397
## F-statistic: 1.397 on 1 and 27 DF, p-value: 0.2476
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "NC", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.47001 -0.23831 -0.07685 0.48753 1.08448
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.21418 1.16794 7.033 1.07e-06 ***
## age -0.04422 0.05061 -0.874 0.393
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6802 on 19 degrees of freedom
## Multiple R-squared: 0.03863, Adjusted R-squared: -0.01197
## F-statistic: 0.7634 on 1 and 19 DF, p-value: 0.3932## [1] "NZMean_96"
## [1] 1
##
## Call:
## lm(formula = formula_str, data = graph_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3092 -0.5810 -0.0906 0.6523 3.6968
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.61348 1.31281 6.561 5.04e-08 ***
## age -0.01525 0.05664 -0.269 0.789
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.112 on 44 degrees of freedom
## Multiple R-squared: 0.001646, Adjusted R-squared: -0.02104
## F-statistic: 0.07252 on 1 and 44 DF, p-value: 0.789
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "PSY", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.17902 -0.51176 0.04751 0.66331 1.74391
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.309072 1.443320 5.757 4.62e-06 ***
## age -0.007259 0.062384 -0.116 0.908
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9574 on 26 degrees of freedom
## Multiple R-squared: 0.0005204, Adjusted R-squared: -0.03792
## F-statistic: 0.01354 on 1 and 26 DF, p-value: 0.9083
##
##
## Call:
## lm(formula = formula_str, data = graph_df[graph_df$diag_group ==
## "NC", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0449 -0.7819 -0.2820 0.7763 3.5092
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.15019 2.57777 3.550 0.00267 **
## age -0.03037 0.11089 -0.274 0.78771
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.358 on 16 degrees of freedom
## Multiple R-squared: 0.004665, Adjusted R-squared: -0.05754
## F-statistic: 0.07499 on 1 and 16 DF, p-value: 0.7877
